Overview

Dataset statistics

Number of variables14
Number of observations24232
Missing cells25292
Missing cells (%)7.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory112.0 B

Variable types

Numeric12
DateTime1
Categorical1

Alerts

targets has constant value "1" Constant
MP1 FLOW1 (l/s) is highly correlated with MP1 UNIDEPTH (mm) and 2 other fieldsHigh correlation
MP1 UNIDEPTH (mm) is highly correlated with MP1 FLOW1 (l/s) and 1 other fieldsHigh correlation
MP1 UpDEPTH_1 (mm) is highly correlated with MP1 FLOW1 (l/s) and 1 other fieldsHigh correlation
Raw Average Velocity (m/s) is highly correlated with MP1 FLOW1 (l/s)High correlation
Daily Cumulative Rainfall (mm) is highly correlated with MP1 FLOW1 (l/s) and 3 other fieldsHigh correlation
MP1 FLOW1 (l/s) is highly correlated with Daily Cumulative Rainfall (mm) and 3 other fieldsHigh correlation
MP1 PDEPTH_1 (mm) is highly correlated with Daily Cumulative Rainfall (mm) and 3 other fieldsHigh correlation
MP1 UNIDEPTH (mm) is highly correlated with Daily Cumulative Rainfall (mm) and 3 other fieldsHigh correlation
MP1 UpDEPTH_1 (mm) is highly correlated with Daily Cumulative Rainfall (mm) and 3 other fieldsHigh correlation
MP1 WATERTEMP_1 (°C) is highly correlated with monthHigh correlation
month is highly correlated with MP1 WATERTEMP_1 (°C)High correlation
MP1 FLOW1 (l/s) is highly correlated with Raw Average Velocity (m/s)High correlation
MP1 UNIDEPTH (mm) is highly correlated with MP1 UpDEPTH_1 (mm)High correlation
MP1 UpDEPTH_1 (mm) is highly correlated with MP1 UNIDEPTH (mm)High correlation
Raw Average Velocity (m/s) is highly correlated with MP1 FLOW1 (l/s)High correlation
df_index is highly correlated with MP1 WATERTEMP_1 (°C) and 3 other fieldsHigh correlation
Daily Cumulative Rainfall (mm) is highly correlated with MP1 FLOW1 (l/s) and 3 other fieldsHigh correlation
MP1 FLOW1 (l/s) is highly correlated with Daily Cumulative Rainfall (mm) and 3 other fieldsHigh correlation
MP1 PDEPTH_1 (mm) is highly correlated with Daily Cumulative Rainfall (mm) and 3 other fieldsHigh correlation
MP1 UNIDEPTH (mm) is highly correlated with Daily Cumulative Rainfall (mm) and 3 other fieldsHigh correlation
MP1 UpDEPTH_1 (mm) is highly correlated with Daily Cumulative Rainfall (mm) and 3 other fieldsHigh correlation
MP1 WATERTEMP_1 (°C) is highly correlated with df_index and 1 other fieldsHigh correlation
Raw Average Velocity (m/s) is highly correlated with df_indexHigh correlation
day is highly correlated with df_index and 1 other fieldsHigh correlation
month is highly correlated with df_index and 2 other fieldsHigh correlation
MP1 FLOW1 (l/s) has 4213 (17.4%) missing values Missing
MP1 PDEPTH_1 (mm) has 4213 (17.4%) missing values Missing
MP1 UNIDEPTH (mm) has 4213 (17.4%) missing values Missing
MP1 UpDEPTH_1 (mm) has 4213 (17.4%) missing values Missing
MP1 WATERTEMP_1 (°C) has 4213 (17.4%) missing values Missing
Raw Average Velocity (m/s) has 4213 (17.4%) missing values Missing
Final Rainfall (mm) is highly skewed (γ1 = 24.17215341) Skewed
df_index has unique values Unique
date has unique values Unique
Daily Cumulative Rainfall (mm) has 15969 (65.9%) zeros Zeros
Final Rainfall (mm) has 23303 (96.2%) zeros Zeros
MP1 FLOW1 (l/s) has 4243 (17.5%) zeros Zeros
Raw Average Velocity (m/s) has 4243 (17.5%) zeros Zeros
hour has 1078 (4.4%) zeros Zeros

Reproduction

Analysis started2021-12-13 10:15:47.299179
Analysis finished2021-12-13 10:16:00.083424
Duration12.78 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct24232
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138161.0091
Minimum16
Maximum254311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size189.4 KiB
2021-12-13T11:16:00.143514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile39074.55
Q1113917.75
median138838.5
Q3144896.25
95-th percentile224423.45
Maximum254311
Range254295
Interquartile range (IQR)30978.5

Descriptive statistics

Standard deviation52066.63521
Coefficient of variation (CV)0.3768547693
Kurtosis0.02691864444
Mean138161.0091
Median Absolute Deviation (MAD)24243
Skewness0.1098546929
Sum3347917572
Variance2710934502
MonotonicityStrictly increasing
2021-12-13T11:16:00.230118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161
 
< 0.1%
1428731
 
< 0.1%
1428821
 
< 0.1%
1428811
 
< 0.1%
1428801
 
< 0.1%
1428791
 
< 0.1%
1428781
 
< 0.1%
1428771
 
< 0.1%
1428761
 
< 0.1%
1428751
 
< 0.1%
Other values (24222)24222
> 99.9%
ValueCountFrequency (%)
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
211
< 0.1%
221
< 0.1%
231
< 0.1%
241
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
2543111
< 0.1%
2543101
< 0.1%
2543091
< 0.1%
2543081
< 0.1%
2543071
< 0.1%
2543061
< 0.1%
2543051
< 0.1%
2543041
< 0.1%
2543031
< 0.1%
2543021
< 0.1%

Daily Cumulative Rainfall (mm)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct267
Distinct (%)1.1%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.204293087
Minimum0
Maximum68.5
Zeros15969
Zeros (%)65.9%
Negative0
Negative (%)0.0%
Memory size189.4 KiB
2021-12-13T11:16:00.408955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.4
95-th percentile6.4
Maximum68.5
Range68.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation4.654601236
Coefficient of variation (CV)3.86500702
Kurtosis109.6808753
Mean1.204293087
Median Absolute Deviation (MAD)0
Skewness9.308247685
Sum29174.00003
Variance21.66531266
MonotonicityNot monotonic
2021-12-13T11:16:00.494142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015969
65.9%
0.21252
 
5.2%
0.41052
 
4.3%
0.5413
 
1.7%
1.2386
 
1.6%
0.1359
 
1.5%
1.4346
 
1.4%
0.3313
 
1.3%
3.8297
 
1.2%
0.8270
 
1.1%
Other values (257)3568
 
14.7%
ValueCountFrequency (%)
015969
65.9%
0.1359
 
1.5%
0.21252
 
5.2%
0.3313
 
1.3%
0.41052
 
4.3%
0.5413
 
1.7%
0.653
 
0.2%
0.784
 
0.3%
0.8270
 
1.1%
0.9137
 
0.6%
ValueCountFrequency (%)
68.51
< 0.1%
68.31
< 0.1%
68.11
< 0.1%
67.91
< 0.1%
67.81
< 0.1%
67.61
< 0.1%
67.41
< 0.1%
67.31
< 0.1%
67.21
< 0.1%
67.11
< 0.1%

Final Rainfall (mm)
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct17
Distinct (%)0.1%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.007500515996
Minimum0
Maximum3.8
Zeros23303
Zeros (%)96.2%
Negative0
Negative (%)0.0%
Memory size189.4 KiB
2021-12-13T11:16:00.570000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3.8
Range3.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0554595147
Coefficient of variation (CV)7.394093251
Kurtosis1176.808331
Mean0.007500515996
Median Absolute Deviation (MAD)0
Skewness24.17215341
Sum181.7
Variance0.003075757771
MonotonicityNot monotonic
2021-12-13T11:16:00.631859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
023303
96.2%
0.1468
 
1.9%
0.2305
 
1.3%
0.457
 
0.2%
0.347
 
0.2%
0.619
 
0.1%
0.59
 
< 0.1%
0.75
 
< 0.1%
0.83
 
< 0.1%
1.22
 
< 0.1%
Other values (7)7
 
< 0.1%
(Missing)7
 
< 0.1%
ValueCountFrequency (%)
023303
96.2%
0.1468
 
1.9%
0.2305
 
1.3%
0.347
 
0.2%
0.457
 
0.2%
0.59
 
< 0.1%
0.619
 
0.1%
0.75
 
< 0.1%
0.83
 
< 0.1%
11
 
< 0.1%
ValueCountFrequency (%)
3.81
 
< 0.1%
2.41
 
< 0.1%
1.81
 
< 0.1%
1.41
 
< 0.1%
1.31
 
< 0.1%
1.22
 
< 0.1%
1.11
 
< 0.1%
11
 
< 0.1%
0.83
< 0.1%
0.75
< 0.1%

MP1 FLOW1 (l/s)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct15742
Distinct (%)78.6%
Missing4213
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean2.914185005
Minimum0
Maximum66.66148
Zeros4243
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size189.4 KiB
2021-12-13T11:16:00.710085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.89527
median2.022915
Q33.599202
95-th percentile7.6190813
Maximum66.66148
Range66.66148
Interquartile range (IQR)2.703932

Descriptive statistics

Standard deviation4.996638831
Coefficient of variation (CV)1.714592184
Kurtosis74.41237956
Mean2.914185005
Median Absolute Deviation (MAD)1.373852
Skewness7.701563242
Sum58339.06961
Variance24.96639961
MonotonicityNot monotonic
2021-12-13T11:16:00.793264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04243
 
17.5%
1.9655262
 
< 0.1%
3.3544132
 
< 0.1%
3.4277212
 
< 0.1%
3.155992
 
< 0.1%
1.9004782
 
< 0.1%
2.7351832
 
< 0.1%
4.3659972
 
< 0.1%
4.0049232
 
< 0.1%
3.2272552
 
< 0.1%
Other values (15732)15758
65.0%
(Missing)4213
 
17.4%
ValueCountFrequency (%)
04243
17.5%
0.1226451
 
< 0.1%
0.1433241
 
< 0.1%
0.2088791
 
< 0.1%
0.2464961
 
< 0.1%
0.3630161
 
< 0.1%
0.418981
 
< 0.1%
0.4252411
 
< 0.1%
0.4329731
 
< 0.1%
0.4344531
 
< 0.1%
ValueCountFrequency (%)
66.661481
< 0.1%
66.615171
< 0.1%
65.487691
< 0.1%
65.319371
< 0.1%
64.445381
< 0.1%
64.306391
< 0.1%
64.231351
< 0.1%
64.133351
< 0.1%
64.050451
< 0.1%
64.036231
< 0.1%

MP1 PDEPTH_1 (mm)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7263
Distinct (%)36.3%
Missing4213
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean65.82876317
Minimum0
Maximum2604.37
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size189.4 KiB
2021-12-13T11:16:00.877942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.749
Q132.285
median55.95
Q368
95-th percentile82.891
Maximum2604.37
Range2604.37
Interquartile range (IQR)35.715

Descriptive statistics

Standard deviation159.8313451
Coefficient of variation (CV)2.427986451
Kurtosis151.2705633
Mean65.82876317
Median Absolute Deviation (MAD)16
Skewness11.85758133
Sum1317826.01
Variance25546.05888
MonotonicityNot monotonic
2021-12-13T11:16:00.963479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.7316
 
0.1%
60.7511
 
< 0.1%
61.9711
 
< 0.1%
25.0910
 
< 0.1%
52.810
 
< 0.1%
55.9810
 
< 0.1%
58.0810
 
< 0.1%
60.7810
 
< 0.1%
62.1710
 
< 0.1%
60.910
 
< 0.1%
Other values (7253)19911
82.2%
(Missing)4213
 
17.4%
ValueCountFrequency (%)
01
< 0.1%
5.711
< 0.1%
5.891
< 0.1%
5.921
< 0.1%
6.151
< 0.1%
6.451
< 0.1%
6.461
< 0.1%
6.532
< 0.1%
6.571
< 0.1%
6.742
< 0.1%
ValueCountFrequency (%)
2604.371
< 0.1%
2597.591
< 0.1%
2584.691
< 0.1%
2570.911
< 0.1%
2568.811
< 0.1%
2552.141
< 0.1%
2551.71
< 0.1%
2535.911
< 0.1%
2500.161
< 0.1%
2481.311
< 0.1%

MP1 UNIDEPTH (mm)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6008
Distinct (%)30.0%
Missing4213
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean67.89456616
Minimum-253.75
Maximum2604.37
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size189.4 KiB
2021-12-13T11:16:01.044957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-253.75
5-th percentile29.61
Q140.125
median49.45
Q366.8
95-th percentile87.303
Maximum2604.37
Range2858.12
Interquartile range (IQR)26.675

Descriptive statistics

Standard deviation159.2273028
Coefficient of variation (CV)2.345214231
Kurtosis153.0028371
Mean67.89456616
Median Absolute Deviation (MAD)13.29
Skewness11.9573398
Sum1359181.32
Variance25353.33395
MonotonicityNot monotonic
2021-12-13T11:16:01.125281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2336
 
0.1%
33.4230
 
0.1%
37.7225
 
0.1%
37.2424
 
0.1%
33.4323
 
0.1%
29.3323
 
0.1%
28.9422
 
0.1%
29.2122
 
0.1%
29.2221
 
0.1%
33.4620
 
0.1%
Other values (5998)19773
81.6%
(Missing)4213
 
17.4%
ValueCountFrequency (%)
-253.751
< 0.1%
12.161
< 0.1%
12.451
< 0.1%
12.681
< 0.1%
12.861
< 0.1%
12.911
< 0.1%
12.951
< 0.1%
13.021
< 0.1%
13.111
< 0.1%
13.271
< 0.1%
ValueCountFrequency (%)
2604.371
< 0.1%
2597.591
< 0.1%
2584.691
< 0.1%
2570.911
< 0.1%
2568.811
< 0.1%
2552.141
< 0.1%
2551.71
< 0.1%
2535.911
< 0.1%
2500.161
< 0.1%
2481.311
< 0.1%

MP1 UpDEPTH_1 (mm)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5936
Distinct (%)29.7%
Missing4213
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean57.14219142
Minimum-253.75
Maximum435.06
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size189.4 KiB
2021-12-13T11:16:01.209376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-253.75
5-th percentile29.61
Q140.14
median49.48
Q366.8
95-th percentile87.303
Maximum435.06
Range688.81
Interquartile range (IQR)26.66

Descriptive statistics

Standard deviation40.5613617
Coefficient of variation (CV)0.7098320995
Kurtosis66.60256449
Mean57.14219142
Median Absolute Deviation (MAD)13.29
Skewness7.451137369
Sum1143929.53
Variance1645.224063
MonotonicityNot monotonic
2021-12-13T11:16:01.293021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2336
 
0.1%
33.4230
 
0.1%
37.7225
 
0.1%
37.2424
 
0.1%
29.3323
 
0.1%
33.4323
 
0.1%
29.2122
 
0.1%
28.9422
 
0.1%
29.2221
 
0.1%
33.4720
 
0.1%
Other values (5926)19773
81.6%
(Missing)4213
 
17.4%
ValueCountFrequency (%)
-253.751
< 0.1%
26.641
< 0.1%
26.781
< 0.1%
26.831
< 0.1%
26.941
< 0.1%
27.211
< 0.1%
27.281
< 0.1%
27.321
< 0.1%
27.412
< 0.1%
27.422
< 0.1%
ValueCountFrequency (%)
435.061
< 0.1%
434.951
< 0.1%
434.771
< 0.1%
434.371
< 0.1%
434.271
< 0.1%
434.21
< 0.1%
434.191
< 0.1%
434.142
< 0.1%
434.131
< 0.1%
434.112
< 0.1%

MP1 WATERTEMP_1 (°C)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct112
Distinct (%)0.6%
Missing4213
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean15.20944103
Minimum-17.8
Maximum21.7
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size189.4 KiB
2021-12-13T11:16:01.379352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-17.8
5-th percentile11.9
Q113.5
median14.8
Q316.2
95-th percentile20
Maximum21.7
Range39.5
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation2.400659687
Coefficient of variation (CV)0.1578400996
Kurtosis1.440033536
Mean15.20944103
Median Absolute Deviation (MAD)1.4
Skewness0.5494477607
Sum304477.8
Variance5.763166932
MonotonicityNot monotonic
2021-12-13T11:16:01.464425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1554
 
2.3%
15.3511
 
2.1%
14.6501
 
2.1%
14.8492
 
2.0%
14.3469
 
1.9%
15462
 
1.9%
14.1449
 
1.9%
13.8425
 
1.8%
13.6409
 
1.7%
15.4400
 
1.7%
Other values (102)15347
63.3%
(Missing)4213
 
17.4%
ValueCountFrequency (%)
-17.81
 
< 0.1%
10.71
 
< 0.1%
10.815
 
0.1%
10.96
 
< 0.1%
1120
 
0.1%
11.128
 
0.1%
11.216
 
0.1%
11.355
0.2%
11.462
0.3%
11.5124
0.5%
ValueCountFrequency (%)
21.73
 
< 0.1%
21.614
 
0.1%
21.514
 
0.1%
21.417
 
0.1%
21.325
0.1%
21.224
0.1%
21.131
0.1%
2139
0.2%
20.934
0.1%
20.850
0.2%

Raw Average Velocity (m/s)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct775
Distinct (%)3.9%
Missing4213
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean0.2156684849
Minimum0
Maximum0.8586
Zeros4243
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size189.4 KiB
2021-12-13T11:16:01.553350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1251
median0.2034
Q30.315
95-th percentile0.477
Maximum0.8586
Range0.8586
Interquartile range (IQR)0.1899

Descriptive statistics

Standard deviation0.158926623
Coefficient of variation (CV)0.7369023946
Kurtosis0.2417926295
Mean0.2156684849
Median Absolute Deviation (MAD)0.0963
Skewness0.5273468258
Sum4317.4674
Variance0.0252576715
MonotonicityNot monotonic
2021-12-13T11:16:01.637124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04243
 
17.5%
0.19882
 
0.3%
0.195382
 
0.3%
0.189980
 
0.3%
0.196279
 
0.3%
0.204379
 
0.3%
0.199877
 
0.3%
0.164777
 
0.3%
0.206175
 
0.3%
0.205275
 
0.3%
Other values (765)15070
62.2%
(Missing)4213
 
17.4%
ValueCountFrequency (%)
04243
17.5%
0.01441
 
< 0.1%
0.0181
 
< 0.1%
0.0271
 
< 0.1%
0.02881
 
< 0.1%
0.03421
 
< 0.1%
0.05851
 
< 0.1%
0.06121
 
< 0.1%
0.0632
 
< 0.1%
0.06841
 
< 0.1%
ValueCountFrequency (%)
0.85861
< 0.1%
0.85591
< 0.1%
0.84781
< 0.1%
0.84511
< 0.1%
0.84241
< 0.1%
0.84061
< 0.1%
0.83881
< 0.1%
0.83791
< 0.1%
0.83521
< 0.1%
0.83341
< 0.1%

hour
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.32481842
Minimum0
Maximum23
Zeros1078
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size189.4 KiB
2021-12-13T11:16:01.710204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.97853455
Coefficient of variation (CV)0.6162160213
Kurtosis-1.216991132
Mean11.32481842
Median Absolute Deviation (MAD)6
Skewness0.02507578135
Sum274423
Variance48.69994446
MonotonicityNot monotonic
2021-12-13T11:16:01.776939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
31115
 
4.6%
21099
 
4.5%
01078
 
4.4%
11073
 
4.4%
41056
 
4.4%
231047
 
4.3%
121034
 
4.3%
151013
 
4.2%
161013
 
4.2%
111005
 
4.1%
Other values (14)13699
56.5%
ValueCountFrequency (%)
01078
4.4%
11073
4.4%
21099
4.5%
31115
4.6%
41056
4.4%
51003
4.1%
6983
4.1%
7950
3.9%
8953
3.9%
9981
4.0%
ValueCountFrequency (%)
231047
4.3%
22970
4.0%
21964
4.0%
20945
3.9%
19974
4.0%
18988
4.1%
171000
4.1%
161013
4.2%
151013
4.2%
141002
4.1%

day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.83336085
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size189.4 KiB
2021-12-13T11:16:01.845008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median15
Q322
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.371194035
Coefficient of variation (CV)0.528706073
Kurtosis-0.9126920805
Mean15.83336085
Median Absolute Deviation (MAD)6
Skewness0.1132623796
Sum383674
Variance70.07688957
MonotonicityNot monotonic
2021-12-13T11:16:01.917559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
161547
 
6.4%
151481
 
6.1%
131265
 
5.2%
171264
 
5.2%
141237
 
5.1%
121175
 
4.8%
30916
 
3.8%
11869
 
3.6%
18865
 
3.6%
29836
 
3.4%
Other values (21)12777
52.7%
ValueCountFrequency (%)
1670
2.8%
2596
2.5%
3582
2.4%
4770
3.2%
5801
3.3%
6572
2.4%
7793
3.3%
8722
3.0%
9432
1.8%
10401
1.7%
ValueCountFrequency (%)
31609
2.5%
30916
3.8%
29836
3.4%
28766
3.2%
27626
2.6%
26792
3.3%
25510
2.1%
24328
 
1.4%
23508
2.1%
22505
2.1%

month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.935622318
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size189.4 KiB
2021-12-13T11:16:02.082350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q35
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.028550339
Coefficient of variation (CV)0.6136106339
Kurtosis-0.1306593407
Mean4.935622318
Median Absolute Deviation (MAD)2
Skewness0.8625602216
Sum119600
Variance9.172117156
MonotonicityNot monotonic
2021-12-13T11:16:02.141830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
56899
28.5%
44772
19.7%
24528
18.7%
102223
 
9.2%
12100
 
8.7%
121161
 
4.8%
91034
 
4.3%
7629
 
2.6%
3551
 
2.3%
11257
 
1.1%
Other values (2)78
 
0.3%
ValueCountFrequency (%)
12100
 
8.7%
24528
18.7%
3551
 
2.3%
44772
19.7%
56899
28.5%
657
 
0.2%
7629
 
2.6%
821
 
0.1%
91034
 
4.3%
102223
 
9.2%
ValueCountFrequency (%)
121161
 
4.8%
11257
 
1.1%
102223
 
9.2%
91034
 
4.3%
821
 
0.1%
7629
 
2.6%
657
 
0.2%
56899
28.5%
44772
19.7%
3551
 
2.3%

date
Date

UNIQUE

Distinct24232
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size189.4 KiB
Minimum2018-12-31 01:20:00
Maximum2021-06-01 00:00:00
2021-12-13T11:16:02.216425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:16:02.302372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

targets
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
24232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
124232
100.0%

Length

2021-12-13T11:16:02.385479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-13T11:16:02.424722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
124232
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-12-13T11:15:58.607962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:48.203048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.112339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:50.064289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.100241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:52.013779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.008062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.904847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.820280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:55.850065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.746949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:57.612730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.680864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:48.277137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.190393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:50.140807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.174876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:52.087364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.081114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.980059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.895436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:55.924418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.816754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:57.685832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.758935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:48.356024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.273498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:50.317005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.255263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:52.167469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.160681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.061112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.977376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.004893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.893485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:57.764610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.836725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:48.435413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.359188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:50.398236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.336177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:52.246508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.238724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.140922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:55.058072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.085374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.969858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:57.940389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.911168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:48.511449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.438673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:50.476878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.413113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:52.322572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.314400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.217710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:55.136326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.160523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:57.042246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.015714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.983625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:48.586026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.517019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:50.553951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.487784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:52.394708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.387517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.291813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:55.211691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.233653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:57.112888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.089222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:59.056181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:48.660857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.594355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:50.630990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.562527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:52.468303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.461438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.366923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:55.384813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.306342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:57.183599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.161964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:59.130819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:48.738231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.674311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:50.712810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.639599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:52.543943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.536352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.443551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:55.462764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.381363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:57.257115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.238116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:59.207919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:48.817672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.756699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:50.793944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.718385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:52.621670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.614942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.522648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:55.542398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.458918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:57.333372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.316943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:59.279542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:48.890785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.833040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:50.870784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.793026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:52.694978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.688016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.597811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:55.619460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.531321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:57.403565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.390341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:59.348074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:48.963382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.908211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:50.945332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.864955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:52.861175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.757345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.669218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:55.692312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.601343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:57.470474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.460584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:59.420500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.038160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:49.987074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.023076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:51.940127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:52.935753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:53.832072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:54.745378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:55.772459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:56.674971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:57.541942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:58.534161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-12-13T11:16:02.471051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-13T11:16:02.607720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-13T11:16:02.757322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-13T11:16:02.892427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-13T11:15:59.559873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-13T11:15:59.738188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-13T11:15:59.910900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-13T11:16:00.013812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexDaily Cumulative Rainfall (mm)Final Rainfall (mm)MP1 FLOW1 (l/s)MP1 PDEPTH_1 (mm)MP1 UNIDEPTH (mm)MP1 UpDEPTH_1 (mm)MP1 WATERTEMP_1 (°C)Raw Average Velocity (m/s)hourdaymonthdatetargets
0160.00.02.42148960.4464.7064.7014.70.1710131122018-12-31 01:20:001
1170.00.00.00000060.5864.4864.4814.70.0000131122018-12-31 01:25:001
2180.00.02.08868259.2664.0264.0214.60.1503131122018-12-31 01:30:001
3190.00.02.20628058.6563.6263.6214.60.1602131122018-12-31 01:35:001
4200.00.02.30797858.8363.4063.4014.60.1683131122018-12-31 01:40:001
5210.00.00.00000058.7963.2063.2014.50.0000131122018-12-31 01:45:001
6220.00.02.70417859.0863.0463.0414.60.1980131122018-12-31 01:50:001
7230.00.02.62268359.3562.9862.9814.50.1926131122018-12-31 01:55:001
8240.00.02.52791860.7961.7961.7914.50.1908231122018-12-31 02:00:001
9250.00.02.43527360.7762.0062.0014.40.1827231122018-12-31 02:05:001

Last rows

df_indexDaily Cumulative Rainfall (mm)Final Rainfall (mm)MP1 FLOW1 (l/s)MP1 PDEPTH_1 (mm)MP1 UNIDEPTH (mm)MP1 UpDEPTH_1 (mm)MP1 WATERTEMP_1 (°C)Raw Average Velocity (m/s)hourdaymonthdatetargets
242222543020.00.00.0000083.8379.5079.5015.40.0000233152021-05-31 23:15:001
242232543030.00.04.3106985.1379.4379.4315.30.2430233152021-05-31 23:20:001
242242543040.00.00.0000085.3679.3579.3515.40.0000233152021-05-31 23:25:001
242252543050.00.00.0000084.3079.1979.1915.40.0000233152021-05-31 23:30:001
242262543060.00.00.0000083.8279.0779.0715.30.0000233152021-05-31 23:35:001
242272543070.00.00.0000083.9278.8878.8815.30.0000233152021-05-31 23:40:001
242282543080.00.00.0000083.6678.7078.7015.30.0000233152021-05-31 23:45:001
242292543090.00.03.9666983.7578.6578.6515.30.2268233152021-05-31 23:50:001
242302543100.00.00.0000083.9078.5478.5415.30.0000233152021-05-31 23:55:001
242312543110.00.00.0000082.9678.4478.4415.20.00000162021-06-01 00:00:001